OpenStreetMap (OSM) is a freely available map of the world to which everyone may contribute geographic information. This makes OSM a rich resource that is diverse with respect to feature variety and scale. At the same time, its data quality is of significant regional variance and also constantly changing over time. OSM’s richness makes it often difficult to assess OSM data quality extrinsically, i.e. by comparing it to external reference data sets because many of OSM’s features are not found in those data sets.

Analysing historical OSM data provides great insight into the evolution of the map. This supports assessing OSM data quality intrinsically, i.e., without comparing to other data sets. However, operating on OSM’s raw full-history data is complex and computationally intensive - especially on a global scale and while there are are several methods and tools for some specific purposes no general purpose software is available for such analyses.

The ohsome data analytics platform eases the analysis of OSM history data by providing high-level interfaces to different spatio-temporal data backends. As its central component, the HeiGIT big spatial data analytics team is developing the OpenStreetMap history database (oshdb), which applies big data technology in order to permit one to deploy the ohsome platform in a scalabe cluster computing environment.

This work is supported by the Klaus Tschira Foundation, Heidelberg. It builds upon earlier and current research on extrinsic and intrinsic OSM data quality analytics of the GIScience Research group and the growing international body of literature.